Explaining MTM concept

MTM is a new emerging stream of literature on TM at the macro-level because the broader field of TM focuses on organisation-level, ignoring the macro-level perspective (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021; Khilji, Tarique, & Schuler, Reference Khilji, Tarique and Schuler2015; Vaiman, Sparrow, Schuler, & Collings, Reference Vaiman, Sparrow, Schuler and Collings2019). MTM is concerned with the attraction, development and retention of skilled workers by governments and nongovernmental institutions to improve outcomes for their citizens (Khilji, Tarique, & Schuler, Reference Khilji, Tarique and Schuler2015). In this study, it is defined as: a set of policies, programmes and actions that nations and their institutions implement to promote the attraction of critical talents (workers with high skill in leadership/top managerial positions and those with mid-skills performing technical roles), the development of their hidden and manifest talents, through investment in formal education, lifelong learning, training, development and empowerment programmes, establishment of quality management training institutions, and use of social and professional networks to equip them with vocational, technical and global knowledge skills, as well as encouraging their retention in order to contribute meaningfully towards the building of their nations for citizens to live fulfilling lives (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021, p. 779).

Skilled and professional individuals are important group of talented workers (Cooke, Reference Cooke and Tarique2021; Lanvin et al., Reference Lanvin, Evans, Lemière, Merdikawati, Lanvin and Evans2013) at the macro-level. By focusing on both high-level skilled and mid-level skilled workers, this definition lends itself to the hybrid philosophy of TM which urges countries to not only focus on high potentials or performers but also take measures to improve the ability of workers who currently are not among the top 10–20 workers (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021) at the macro-level. The promotion of the macro view of TM is necessary to broaden its scope beyond its current focus at individual and firm levels.

MTM is different from global talent management (GTM). While GTM is concerned with micro- and meso-level management of talents in the global arena, the focus of MTM is at the country or macro-level, thus, both within a single country and/or across countries (Khilji, Tarique, & Schuler, Reference Khilji, Tarique and Schuler2015; Vaiman et al., Reference Vaiman, Sparrow, Schuler and Collings2019). Besides, policies/programmes to boost MTM involve giving considerations to labour market, legal and political climate, educational system and pathways for thoughtful career development initiatives as macro-environmental factors can enable or hinder TM efforts (Khilji, Tarique, & Schuler, Reference Khilji, Tarique and Schuler2015; Lanvin & Monteiro, Reference Lanvin and Monteiro2019), and the harnessing of its sustainable dividends such as improved national wellbeing (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021).

Linking MTM to national wellbeing

Wellbeing is often conceptualised broadly from the objective and subjective perspectives (Anand, Reference Anand2016; Grant, Christianson, & Price, Reference Grant, Christianson and Price2007; Voukelatou et al., Reference Voukelatou, Gabrielli, Miliou, Cresci, Sharma, Tesconi and Pappalardo2021). Objective wellbeing describes the extent of success in possession of material resources such as income, food and housing (Grant, Christianson, & Price, Reference Grant, Christianson and Price2007). However, subjective wellbeing is concerned with how people cognitively evaluate their own lives (life satisfaction), and state of happiness (Anand, Reference Anand2016). This study focused on both subjective and economic (objective wellbeing) measures at the macro-level.

Empirically and theoretically, MTM strategies can improve national wellbeing through various channels. First, Diebolt and Hippe (Reference Diebolt and Hippe2019) empirically found that past regional human capital significantly explained the differences in innovation and economic development. They argued, ‘an increase in human capital may induce a rise in the number of innovative entrepreneurs and products, thereby indirectly spurring economic development through the channel of innovation’ (p. 542). In Asia, Islam’s (Reference Islam2020) panel data results suggested that human capital (measured by adult literacy rate) significantly enhances economic wellbeing. Recently, in Europe, McGarry et al. (Reference McGarry, Krisjane, Sechi, MacÉinrí, Berzins and Apsite-Berina2021) suggested that highly qualified migrants experienced higher life satisfaction.

While findings are useful, they neglect the potential impact of MTM on national wellbeing. An exception to this though, is the study by Anlesinya et al. (Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021) that examined the macro impacts of MTM in developing countries. Their result showed decent work responds positively to MTM. More revealing is the finding that decent work plays an important intermediating role in the nexus between national wellbeing and MTM. Thus, this study contended that MTM practices should lead to enhancement in subjective national wellbeing because people enjoy applying their talents, and are happy that they have talents (technical and global knowledge competences) that can be utilised to overcome hardships and setbacks in life, resulting in higher life satisfaction. These are similar to the propositions of the HCT.

The HCT proposes that human capital investments are capable of generating returns for improving the lot of humans in terms of enhanced social welfare (Bagdadli et al., Reference Bagdadli, Gianecchini, Andresen, Cotton, Kaše, Lazarova and Dello Russo2021; Becker, Reference Becker1964; Tan, Reference Tan2014). In particularly, MTM can promote economic prosperity because well-trained and developed individuals are more productive and innovative, resulting in the development of new socio-economic solutions. It may also endow people with management skills and technological knowledge for easy identification and exploitation of new business opportunities for the benefits of all (see Diebolt & Hippe, Reference Diebolt and Hippe2019; Tan, Reference Tan2014; Teixeira & Queirós, Reference Teixeira and Queirós2016). Accordingly, from the HCT perspective, this study argues that MTM investments can increase economic wellbeing of people, as they can provide them with opportunities of earning more future income.

Similarly, it is generally known that many developing nations’ economy, educational and health systems tend to face major challenges because there are inadequate talented and well-trained professionals (see Cooke, Reference Cooke and Tarique2021) to develop innovative and sustainable solutions to address their socio-economic challenges. Consequently, from the HCT’s perspective, this study contends that MTM practices can enhance wellbeing of the larger population and not just only for high-skilled and mid-skilled people or beneficiaries of MTM investments. The reason being that MTM practices can develop the talent of and nurture diverse-skilled professionals who would become more accessible to build their nations and contribute to global success through the application of their talent in solving socio-economic challenges that hinder the wellbeing of the larger population. For instance, if research and scientific talents apply their expertise and specialised knowledge to develop vaccines to the raging COVID-19 and future health pandemics, it will not only benefit them alone, but the larger population or humanity will also benefit from improved health and wellbeing as a result. Likewise, if entrepreneurial talents develop measures or generate innovative solutions to spur job creation and economic development, they will be improving their own wellbeing and that of the members of the general population.

Moreover, Grant, Christianson, and Price (Reference Grant, Christianson and Price2007) theoretically posited that management practices can have wellbeing synergies or trade-offs impacts – that is mixed impact. For instance, they argued that job rotation training, through skills variety and enriched jobs, will increase psychological wellbeing but also create physical wellbeing problem. Likewise, due to enhanced earning prospects, reward systems will improve economic wellbeing but can harm social wellbeing due to competitive behaviours, which erode trust. Recently, Ogbonnaya and Messersmith (Reference Ogbonnaya and Messersmith2019) and Gillet et al. (Reference Gillet, Huyghebaert-Zouaghi, Austin, Fernet and Morin2021) have similarly found in the mainstream HRM field that HRM practices have mixed effects on employee wellbeing. However, this claim has not been empirically tested in the MTM literature and national wellbeing research. Hence, through the lenses of the HCT and related empirical literature, this study anticipates a synergistic impact of MTM rather trade-offs impact on national wellbeing, and hypothesised that:

Moderating role of NRE in MTM and national wellbeing

Prior evidence suggests that NRE can better or harm both national subjective and economic wellbeing. For instance, Muhanji, Ojah, and Soumaré (Reference Muhanji, Ojah and Soumaré2019), using panel data from selected African countries, found that national economic wellbeing improves in mineral/oil-rich countries as they make them to earn more foreign exchange to fund public and social investments. However, aggregated natural resource rents diminished societal wellbeing and they attributed this to the low foreign exchange earning potential of non-mineral/oil commodity exports. In contrast, Lu and Sohail (Reference Lu and Sohail2022) showed that natural capital contributes positively to happiness, health and overall human wellbeing over time in China. Similarly, Apergis and Majeed (Reference Apergis and Majeed2021) suggested in a study of 95 countries that green natural view endowment improves national happiness.

Thus, this mixed effect of NRE (Apergis & Majeed, Reference Apergis and Majeed2021; Lu & Sohail, Reference Lu and Sohail2022; Muhanji, Ojah, & Soumaré, Reference Muhanji, Ojah and Soumaré2019) on national wellbeing, as well as the significant impact of human capital (see Islam, Reference Islam2020; McGarry et al., Reference McGarry, Krisjane, Sechi, MacÉinrí, Berzins and Apsite-Berina2021) and MTM (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021) on national wellbeing discussed earlier suggest that NRE can moderate the link between MTM and national wellbeing. However, the controversy of whether NRE is a curse (Mohtadi, Reference Mohtadi2022; Rahim et al., Reference Rahim, Murshed, Umarbeyli, Kirikkaleli, Ahmad, Tufail and Wahab2021; Sachs & Warner, Reference Sachs and Warner2001) or blessing (Alexeev & Conrad, Reference Alexeev and Conrad2009; Hussain et al., Reference Hussain, Ye, Usman, Mir, Usman and Rizvi2020; Islam, Zhang, & Managi, Reference Islam, Zhang and Managi2022) makes it unclear whether NRE and MTM can interactively spur or hamper national wellbeing.

Sachs and Warner (Reference Sachs and Warner1995) started the argument of resource curse. In their study, they found robust negative effect of NRE on economic growth. Later empirical studies, which confirmed the results of Sachs and Warner, led Sachs and Warner (Reference Sachs and Warner2001) to conclude that the empirical evidence of resource is not a bulletproof, but it is quite strong. However, Alexeev and Conrad (Reference Alexeev and Conrad2009) have strongly rejected the claim of resource curse.

One of the transmission channels that is central to this controversy is HCD. According to Sachs and Warner (Reference Sachs and Warner1999), resource abundance leads to the neglect of investment in human capital, as it gives false sense of financial and economic security to governments. Gylfason (Reference Gylfason2001) provided strong empirical support, claiming, ‘their natural wealth may blind them to the need for educating their children’. They found that gross secondary enrolment; expected years of schooling for girls and public expenditure on education have inverse relationship with the share of natural capital in national wealth. Thus, natural capital crowds-out human capital. Birdsall, Pinckney, and Sabot (Reference Birdsall, Pinckney, Sabot and Auty2001) argued that investment in education was limited due to lack of money. Therefore, resource abundance should help remove the constraint on human capital investment. However, the data showed otherwise. They found that resource-abundant countries, on average, invest less in education compared with resource-poor countries. Recently, Mohtadi (Reference Mohtadi2022) employed longitudinal data from 161 countries and using GMM analysis indicated total resource rents oppress quality-adjusted human capital accumulation, indicating a crowding-out effect.

However, Stijns (Reference Stijns2006) questioned the resource curse evidence by asking, why are rich-parents children more likely to be highly educated despite the possibility of wasting a fair share of their wealth on invaluable? According to Stijns (Reference Stijns2006), there is a potential ‘wealth effect’ that might be explaining this outcome. Synonymously, the author argued that similar ‘wealth effects’ could make resource-rich countries invest more in human capital despite spending considerable amounts on other things. In their empirical study, the author found that mineral wealth has a significant and positive effect on human capital. A one-dollar shock in resource rent generates about five cents of extra educational expenditure per year. Recently, Rahim et al. (Reference Rahim, Murshed, Umarbeyli, Kirikkaleli, Ahmad, Tufail and Wahab2021) indicated that human capital and NRE can collectively enhance economic welfare and further noted that HCD buffers against the impacts of resource curse on economic growth in the Next Eleven countries. Also, in Pakistan, Islam, Zhang, and Managi (Reference Islam, Zhang and Managi2022) revealed that while human capital base of the country has expanded over the years through certain government policies, natural capital has decreased. They therefore warned that failure to arrest the trade-offs between human capital and natural capital accumulation could have dire impacts on economic welfare.

Given that TM correlates with HCD, it is reasonable to hypothesise from the above that, NRE is a macro-level boundary condition that can strengthen or weaken the effect of MTM on national wellbeing. Thus, while the HCT focuses solely on intangible resources such as macro talent in realising socio-economic outcomes such as subjective and economic wellbeing, it is possible for physical resources such as NRE to serve as a boundary condition for MTM by having a substitution or complementary effects on MTM and its outcomes. Consequently, this study seeks to make theoretical contribution by incorporating NRE as a boundary condition into the HCT in understanding national wellbeing impacts of MTM. Hence:

Convergence in national wellbeing

Divergence in wellbeing can lead to major conflict because those who felt left behind often become frustrated and develop a sense of them being deliberately deprived (Anlesinya, Reference Anlesinya2021; Neumayer, Reference Neumayer2003). Hence, convergence along various parameters of human wellbeing is of a major interest for academics and policy makers alike (see Neumayer, Reference Neumayer2003; Palma & Reis, Reference Palma and Reis2019; Pereira, Camanho, Marques, & Figueira, Reference Pereira, Camanho, Marques and Figueira2021). According to Neumayer (Reference Neumayer2003), people are eager to see their level of economic wellbeing improve but also converging to that of the wealthiest. Thus, convergence is when the gap between best and worst performers declines over time (Pereira et al., Reference Pereira, Camanho, Marques and Figueira2021). Accordingly, in this study, national wellbeing convergence is operationalised as a situation whereby countries with low wellbeing will see their wellbeing increase at a faster rate compared to countries with better wellbeing, leading to reduction in wellbeing differences/gaps between countries, or regions over time. Convergence referred here is ‘conditional’ and not ‘absolute’. The latter assumes a common steady-state equilibrium for all countries, while the former assumes different steady-state conditions, driven by country-specific observed or unobserved heterogeneities.

Empirically, Pereira et al. (Reference Pereira, Camanho, Marques and Figueira2021) assessed convergence in SDG 3 performance among the six World Health Organisation (WHO) regions from 2016 to 2020, and found that while two regions (Americas and Eastern Mediterranean) were partially convergent, two were fully divergent (South-East Asia and Africa). However, the other two (Western Pacific and European) maintained a stable wellbeing performance. Also, Palma and Reis (Reference Palma and Reis2019) showed that economic wellbeing in Portugal was behind that of most Western European economies prior to the 18th century but convergence was achieved by mid-18th century. Earlier, Raiser, Wes, and Yilmaz (Reference Raiser, Wes and Yilmaz2016) found that Poland and Turkey have joined high-income countries (convergence in income) in the European Union through the benefits of global integration, macroeconomic discipline and private entrepreneurship. Similarly, Cuaresma, Havettová, and Lábaj (Reference Cuaresma, Havettová and Lábaj2013) showed that HCD has emerged as a major contributor to accelerating income convergence process among emerging economies in Europe.

Moreover, the heterogeneity that exists among regions implies that the issue of convergence path should be approached not from a global perspective, as this hides prevalent regional dynamics but regional convergence. However, only a few studies have investigated regional convergence. For instance, Peiró-Palomino’s (Reference Peiró-Palomino2019) empirical evidence indicated that great disparities exist in economic convergence across the Organisation for Economic Co-operation and Development regions with no signs of convergence over the periods investigated but the regions were differentiated by two clubs of low and high wellbeing. Likewise, in Italy, Ferrara and Nisticò (Reference Ferrara and Nisticò2012) found that the level of dispersion in economic wellbeing among the Italian regions has shrunk over the periods analysed, even though the ranking of the regions has not changed substantially. Prior to this, Rentfrow, Mellander, and Florida’s (Reference Rentfrow, Mellander and Florida2009) state-level comparison of wellbeing in the USA showed the existence of regional differences in wellbeing, with higher wellbeing reported in states where people are wealthier, better educated, more tolerant and emotionally stable compared to residents of states with comparatively low well-being levels. Other studies also indicated that wellbeing is higher in regions that are characterised by high acceptance for democratic values (Stevenson & Wolfers, Reference Stevenson and Wolfers2008), and regional integration (Cabral & Castellanos-Sosa, Reference Cabral and Castellanos-Sosa2019).

Although the above studies have contributed to enhancing the knowledge of possible convergence in wellbeing, they have focused mainly on economic (objective) wellbeing to the neglect of subjective wellbeing indicators such as life satisfaction. Likewise, while these studies provide great insight about potential differences in wellbeing by geographical regions, there is dearth of empirical evidence on regional variations in the paths of national wellbeing convergence. Consequently, this study sought to extend the current wellbeing convergence discourse by arguing that:

Conceptual framework

The study’s hypotheses are summarised in Figure 1. Through the lenses of the HCT and the extant related empirical literature, the first hypothesis argued that MTM will synergistically enhance national subjective (hypothesis 1a) and economic wellbeing (hypothesis 1b) as it can simultaneously create more prosperous and happier individuals. Moreover, because earnings generated from the exploitation of natural resources can increase investment in macro talent accumulation to better the wellbeing of citizens, this study contended through the HCT’s perspective and the associated ‘wealth effect’ view of abundant natural capital that, the direct effect of MTM on national subjective (hypothesis 2a) and economic wellbeing (hypothesis 2a) will be greater in natural resource-rich countries relative to natural resource-poor countries. Besides, as countries strive to achieve the SDG 3, it is important to show the extent to which past underperformers in national wellbeing are catching-up with high performers, and to bring in management dimension of the wellbeing convergence literature. Convergence at the global level may be a far-fetch objective to achieve compared with convergence paths in regions but studies on regional convergence in wellbeing are significantly limited. Hence, hypothesis 3 tested for convergence paths in national subjective wellbeing (hypothesis 3a) and economic wellbeing (hypothesis 3b) for the global sample as well as within the four major regions of the world. Thus, it is expected that past national wellbeing will have a significant negative impact on the growth of or changes in national wellbeing as it will suggests that the gap between best and worst performers in wellbeing are declining over time, and hence, existence of evidence of convergence. As noted earlier, this study focuses on ‘conditional’ and not ‘absolute’ convergence – convergence is conditioned on certain conditions (in this case, MTM, NRE and the socio-economic variables which are used as control variables). Finally, this study controlled for the effects of different socio-economic factors, and year-fixed effects (see methodology for their justifications).

Fig. 1.

Conceptual framework.

Research design and sample

This study adopted panel research design. Panel research is basically a type of research that utilised a data collected over a meaningful period of time (Baltagi, Reference Baltagi2005; Hsiao, Reference Hsiao2022). This design was used because; first, it increases confidence in drawing conclusions about causality. Second, wellbeing changes are time varying. Hence, the panel technique is appropriate as it can show how TM strategies capture changes in national wellbeing over time (see Baltagi, Reference Baltagi2005; Hsiao, Reference Hsiao2022). Third, the empirical TM literature is replete with cross-sectional design (Anlesinya, Dartey-Baah, & Amponsah-Tawiah, Reference Anlesinya, Dartey-Baah and Amponsah-Tawiah2019), which is incapable of handling intertemporal dynamics and unobserved country-specific heterogeneities, as well as omitted variable bias (Hsiao, Reference Hsiao2022; Zhang & Adom, Reference Zhang and Adom2018). Panel design addresses concerns of intertemporal dynamics and unobserved heterogeneities.

In terms of sample and data, this study focused on developing countries because they faced greater problems with human wellbeing compared to their developed counterparts (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021). It used unbalanced panel data from 2013 to 2019 from 78 developing countries from the four main regions globally (see Appendix A). The World Bank (2019a) defined developing countries on the basis of income as countries within the middle- and lower-income categories. The 2013 as the index year was based on data availability. Also, the expectation maximisation method was applied to compute for missing values as it has the capacity to handle different sample sizes and percentage of data missing to produce more efficient estimates (Gold & Bentler, Reference Gold and Bentler2000). This consequently, has converted the unbalanced panel data to a balanced panel of 546 country-year observations. Although unbalanced panel is a norm in econometric settings, it is easier to handle balanced panels (Baltagi, Reference Baltagi2005; Hsiao, Reference Hsiao2022).

Study variables

MTM was measured using the Institut Europeen d’Administration des Affaires’s global talent competitiveness index (GTCI) (Lanvin & Monteiro, Reference Lanvin and Monteiro2019) consistent with Anlesinya et al. (Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021). The GTCI is appropriate for the following reasons: first, organisations use attraction–development–retention framework for managing their talented employees. The GTCI is measured within this framework too (Lanvin et al., Reference Lanvin, Evans, Lemière, Merdikawati, Lanvin and Evans2013). Second, with differentiation (i.e., either purely exclusive or hybrid focus) being a key feature of TM theory, the GTCI’s construction is in line with the workforce differentiation principle (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021; Lanvin et al., Reference Lanvin, Evans, Lemière, Merdikawati, Lanvin and Evans2013), and it adopts the hybrid TM focus (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021). The GTCI measures a set of practices or actions by governments to promote TM in their country based on a set of inputs and outputs, with the average composite score ranging from 0 to 100. Thus, the GTCI is an input–output model within the attract–develop–retain framework employed by multinational corporations in managing their organisational talents. The input aspect of the GTCI has four dimensions: enable pillar (measuring the regulatory, market, business and labour environments within a country); attract pillar (assesses external openness in the form of brain gain, and attraction of businesses, and people; and internal openness in the form of tolerance of minorities and migrants, social mobility, social inclusion and gender development equality); grow pillar (measures enrolment and quality of formal education, lifelong learning, access to growth opportunities and collaborations) and retain pillar (assesses countries’ efforts regarding social and human sustainability, proxy by brain retention, pension systems and personal safety within a country). The GTCI’s two output dimensions include: the vocational and technical skills; and global knowledge skills pillars. The vocational and technical skills pillar measures countries’ mid-level skills in terms of availability of technicians and associate professionals; and employability performance in terms of the ease of finding skilled employee, relevance of education system to the economy, and skills matching with both tertiary and secondary educations. Finally, the global knowledge skills measure a country’s high-level skills in terms of senior officials and managers, researchers, scientists, engineers and professionals, workforce and population with tertiary education; and talent impacts as measured by innovative outcomes, new product entrepreneurial activity, new business intensity and number of scientific journal articles.

National wellbeing: National wellbeing is operationalised in this study from the two main classifications of wellbeing measures: objective and subjective measures. The use of both subjective wellbeing and economic wellbeing is essential because wellbeing is a multi-dimensional phenomenon (Anand, Reference Anand2016; Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021).

Objective national wellbeing: economic wellbeing was measured using gross domestic product (GDP) per capita and it was sourced from the World Development Indicator (WDI) database. Although the current measurement of GDP is not comprehensive as it should be, it is still a very useful measure of economic wellbeing as it is able to capture changes in economic wellbeing (Dynan & Sheiner, Reference Dynan and Sheiner2019).

Subjective national wellbeing was measured using life satisfaction. Life satisfaction does very well in capturing the psychological and subjective health domains of people’s lives. In this study, life satisfaction was measured using the Cantril Life Ladder from the World Database of Happiness. Life Ladder data are collected by the Gallup World Poll (GWP) after the posing of the following question to global respondents; ‘please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?’ (Helliwell, Huang, & Wang, Reference Helliwell, Huang, Wang, Helliwell, Layard and Sachs2017, p. 1). Cantril ladder of life is a purer and more useful measure of life evaluation (Kahneman & Deaton, Reference Kahneman and Deaton2010) and has been used by prior studies (e.g., Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021; Sachs, Reference Sachs, Helliwell, Layard and Sachs2016). It is important to recognise that subjective wellbeing (i.e., life satisfaction) was collected using a single-item scale. Single item ‘scales are not the best measures of wellbeing available, but they are very widely used in international surveys and have been found to have acceptable levels of reliability and validity’ (Anand, Reference Anand2016; Diener, Suh, Lucas, & Smith, Reference Diener, Suh, Lucas and Smith1999, pp. 277–278).

NRE was proxy using the total amount of natural resource rent as percentage of GDP and it is sourced from the WDI database. A natural resource rent refers to the revenues that countries earn from the extraction of their natural resources. It measures the extent to which a country is endowed with natural resources (Shahbaz, Destek, Okumus, & Sinha, Reference Shahbaz, Destek, Okumus and Sinha2019). Specifically, total natural resources rents are the ‘sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents’. This economic rent is not synonymous with rents from property income, which involves making payments to landowners by a tenant (World Bank, 2020).

Control variables: First, the study controlled for country size, using log of population size in terms of the total number of residents in a country irrespective of their citizenship and legal status. According to the World Bank (2019b), significant increases in population size can have negative impact on natural resources and social infrastructure, leading to negative impact on wellbeing. This is because, higher population growth will worsen the demand for food, energy, water, social services and infrastructure. Again, MTM and NRE may correlate with some unobserved regional specific time-invariant characteristics. Consequently, the study adjusted the model to include regional effects, using regional dummies, whereby; if a country is from the ‘Africa region = 1, and 0 = otherwise; Latin America and Caribbean (LAC) = 1, and 0 = otherwise; Asia = 1, and 0 = otherwise’, with Europe being the reference group.

The study also controlled for inflation using the Consumer Price Index to gauge the effect of macroeconomic stability as it can affect MTM performance and wellbeing. Tourism using the number of international tourist arrivals was included as additional controls because it is generally noted to affect skill development, knowledge transfer, wellbeing and economic growth. Next, the study controlled for employment using employment ratio. Employment ratio describes the percentage of a nation’s population of working age that is employed. The data on inflation, tourism and employment were sourced from the WDI database of the World Bank. The effect of social support was accounted for because previous studies such as Helliwell, Huang, Wang, and Norton (Reference Helliwell, Huang, Wang, Norton, Helliwell, Layard, Sachs, De Neve, Aknin, Huang and Wang2020) showed that it is major determinant of wellbeing. In this study, social support is the extent to which people have someone to count on in difficult times. It is a national average of the binary response (either 0 or 1) to the GWP question ‘If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?’ The social support data were sourced from the World Database of Happiness.

Empirical model

To test the first two hypotheses in this study, two main equations were estimated. Equation 1 shows the model of level national wellbeing and MTM, controlling for the interaction of MTM and NRE. ‘d’ captures regional dummies, and ‘Year dummies’ captures year-fixed effects. From Equation 1, if NRE is complementary with MTM, then the coefficient of the interactive term (i.e., ‘δ’) should be positive and statistically significant. Otherwise, natural capital may be seen to crowd-out macro talent. Inclusion of the interaction of MTM and NRE in the model while having in addition to their respective levels included in the same model creates identification problem (i.e., multicollinearity). To address this concern, this study mean-centred MTM and NRE variables. Further, demeaning MTM and NRE improves the interpretability of the direct effect of MTM as no country reported zero NRE in the actual data.

(1)$$\eqalign{\log ( NWB_{it}) = & \; \alpha _0 + \beta \log ( MTM_{it}) + \gamma log( NRE_{it}) + \delta log( MTM_{it}) \ast log( NRE_{it}) \cr & + log( Country\;size_{it}) + log( Inflation_{it}) + \log ( Employment_{it}) \cr & + \log ( Tourism_{it}) + Social\;support_{it} + d_i + Year\;dummies + \varepsilon _{it}} $$

The postulated relationship in Equation 1 could be confounded due to omitted variable bias. To address this concern and increase the robustness of the estimates generated by the statistical models, this study controlled for a range of socio-economic factors to address potential omitted variable bias, such as country size, inflation, employment level, tourism, social support and year dummies to address time varying common shocks and business cycle effects.

Furthermore, following prior researchers (e.g., Adom & Adams, Reference Adom and Adams2020; Neumayer, Reference Neumayer2003), the study adopted the β-convergence procedure for testing for convergence in national wellbeing, which involves estimating Equation 2. The β-convergence tests whether countries or individuals with low performance in the past have performed comparatively better to past high performers. ‘β-convergence’ exists when the estimated ‘β’ in Equation 2 is negative and statistically significant. Without the controlled variables (i.e., MTM and population), the estimated β reflects absolute β-convergence, which assumes the same steady-state condition for all countries. However, it becomes conditional once we control for MTM, NRE and other socio-economic factors. Of particular importance, here is how the ‘β’ coefficient evolves as we control for MTM. If MTM is relevant in propelling convergence path, we should expect an increase in the ‘β’ coefficient once we control for MTM. Consequently, in the result section, the study first estimated Equation 2 without the controls and then later introduced the control variables. Equation 2 shows the equation for estimating β-convergence.

(2)$$\eqalign{\log \left({\displaystyle{{NWB_{it}} \over {NWB_{i, t-1}}}} \right) = & \; \alpha + \beta \log ( NWB_{i, t-1}) + \delta log( MTM_{it}) + \gamma log( NRE_{it}) \cr & + \theta log( Country\;size_{it}) + log( Inflation_{it}) + \log ( Employment_{it}) \cr & + \log ( Tourism_{it}) + Social\;support_{it} + d_i + Year\;dummies + \varepsilon _{it}} $$

Estimation technique

The postulated relationships in both Equations 1 and 2 assume causality from MTM to national wellbeing measures. However, improvement in national wellbeing could also influence TM. Economic prosperity affects multinational, small, large and regional demand for and their ability to recruit and retain talent workers. Economies experiencing economic growth are better positioned compared to the compatriot to expand and invest in talents. The Towers Watson Global Talent Management and Rewards survey, which included 1,176 global companies, revealed that cuts during recession limit the companies to engage employees and also to recruit top talentFootnote ¹. This potential reverse causality problem confounds the relationship weakening the claim of causality from MTM to national wellbeing. The simultaneous two-step dynamic system GMM estimator is adopted as the main estimation technique due to its superiority in dealing with heteroscedasticity errors and endogenous regressors. The GMM technique relies on lags of endogenous variables as instruments. In this context, it means using lags of MTM to instrument MTM. Using lags of MTM as instrument variables breaks the causality from national wellbeing as current national wellbeing does not influence previous MTM. However, previous MTM can influence current national wellbeing. Following previous scholars, the number of lags is limited to a maximum of three because higher lags generally can have adverse effect on GMM instrumentation (Adom, Agradi, & Vezzulli, Reference Adom, Agradi and Vezzulli2021).

Arellano and Bond (Reference Arellano and Bond1991) proposed GMM estimator (difference GMM model) in which lagged exogenous variables are used as instruments for the first differenced, lagged outcome variable to address endogeneity problems. But it was established later that these lagged variables tend to provide insufficient information regarding the first differences (Arellano & Bover, Reference Arellano and Bover1995; Blundell & Bond, Reference Blundell and Bond1998). Hence, Blundell and Bond (Reference Blundell and Bond1998), building on Arellano and Bover’s (Reference Arellano and Bover1995) work, proposed a system estimator that leverages further moment conditions (internal controls) on both first differences as well as levels, with lagged first-differences of the series utilised as instruments in the levels equation. The system GMM estimator mitigates potential bias associated with finite samples and the difference estimator. Both the first difference, and system GMM may be estimated using either one-step or two-step methods (Blundell & Bond, Reference Blundell and Bond1998). This study used the simultaneous two-step system GMM estimator as it utilises residuals from the one-step estimates to produce estimates that are more efficient than estimates of one-step system GMM estimator (see Adom, Agradi, & Vezzulli, Reference Adom, Agradi and Vezzulli2021; Arellano & Bover, Reference Arellano and Bover1995; Blundell & Bond, Reference Blundell and Bond1998). Moreover, it has the capacity to control for unobservable country-specific effects by allowing for the inclusion of country-specific fixed effects and regional dummies (Arellano & Bover, Reference Arellano and Bover1995; Blundell & Bond, Reference Blundell and Bond1998). It is also suitable for a short panel (Diaz-Fernandez, Bornay-Barrachina, & Lopez-Cabrales, Reference Diaz-Fernandez, Bornay-Barrachina and Lopez-Cabrales2017) like the 7-years as used in this present study.

Importantly, this study utilised the GMM estimator over the static panel data estimators (fixed-effect and random-effect) because human wellbeing is dynamic over a period and can be appropriately tracked using a dynamic rather than static panel data estimators.

Theoretical implications

Albeit business and management schools have been researching and training students on sustainability and corporate social responsibility to promote responsible business practices and professional behaviours, management scholarship targeted at addressing grand societal problems have not received the needed attention. Even the few contributions of management and business researchers to the realisation of the SDGs still remain highly fragmented (Macht, Chapman, & Fitzgerald, Reference Macht, Chapman and Fitzgerald2020; Pizzi, Caputo, Corvino, & Venturelli, Reference Pizzi, Caputo, Corvino and Venturelli2020). As a result, management practices (George et al., Reference George, Howard-Grenville, Joshi and Tihanyi2016; Macht, Chapman, & Fitzgerald, Reference Macht, Chapman and Fitzgerald2020; Voegtlin et al., Reference Voegtlin, Scherer, Stahl and Hawn2022) such as TM (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021; Thunnissen, Boselie, & Fruytier, Reference Thunnissen, Boselie and Fruytier2013) are increasingly being postulated lately as useful means for achieving wellbeing (SDG 3), which is, a critical grand societal agenda for most countries, particularly in developing countries, but research on the effect of TM on wellbeing at the macro-level and its potential contextual condition of NRE is very limited. Hence, this study assesses the direct impact of MTM on national subjective and economic wellbeing and further investigates the moderating role of NRE as well as rates of convergence in national wellbeing.

As MTM is a new emerging stream of literature on TM at the macro-level (Khilji, Tarique, & Schuler, Reference Khilji, Tarique and Schuler2015; Vaiman et al., Reference Vaiman, Sparrow, Schuler and Collings2019) but highly deficient in empirical knowledge (Anlesinya et al., Reference Anlesinya, Amponsah-Tawiah, Adom, Damoah and Dartey-Baah2021), this empirical evidence is a vital contribution to knowledge on MTM and national wellbeing, and in extending the limited and fragmented SDG-related management research. Again, this evidence on national wellbeing convergence equally provides a major theoretical insight for even organisational TM and HRM scholars to begin to explore convergence in employee wellbeing through organisational TM practices because not all employees in a given organisation may have achieved the same level of wellbeing but such empirical knowledge is scarce.

Besides, this study contributes to the HCT by extending its application at the macro-level and by demonstrating that countries can achieve national wellbeing synergies because of MTM practices rather trade-offs impact. This is an important theoretical insight in the sense that it validates MTM as a channel for improving different domains of national wellbeing (subjective and economic wellbeing). This consequently, adds to the emerging wellbeing synergy versus trade-offs literature led by Grant, Christianson, and Price (Reference Grant, Christianson and Price2007) and affirmed by Gillet et al. (Reference Gillet, Huyghebaert-Zouaghi, Austin, Fernet and Morin2021) and Ogbonnaya and Messersmith (Reference Ogbonnaya and Messersmith2019) in the mainstream HRM literature.

Moreover, there is a dearth of empirical studies on the boundary conditions of HCT and its outcomes even though calls to investigate its contextual factors/moderators have been persistent (Bagdadli et al., Reference Bagdadli, Gianecchini, Andresen, Cotton, Kaše, Lazarova and Dello Russo2021; Tan, Reference Tan2014). Hence, this study contributes theoretically by revisiting the topical issue of resource curse-blessing within the MTM field. This study incorporates NRE as a macro-level boundary condition into the HCT and demonstrates that natural capital did not crowd-out macro talent but rather they jointly contribute to better national subjective and economic wellbeing. This could mean that tapping abundant natural capital can make resources available to boost macro talent accumulation on the one hand, and expanded macro talent base may be helpful in lessening resource-curse impacts on the other, thereby improving different facets of national wellbeing. The current evidence therefore provided new evidence to affirm that the associated ‘wealth effect’ of NRE (see Hussain et al., Reference Hussain, Ye, Usman, Mir, Usman and Rizvi2020; Stijns, Reference Stijns2006) facilitates macro talent accumulation to enhance different national wellbeing facets.

Furthermore, this study makes a novel contribution because the existing wellbeing convergence literature largely neglects knowledge from the management fields and subjective dimension of wellbeing. This study shows that countries with low subjective wellbeing and economic wellbeing are gradually catching-up with those with better levels of wellbeing in course of time with MTM also facilitating the convergence process. This is a clear departure from previous knowledge on national wellbeing convergence that focused on economic wellbeing.

Practical and policy implications

Developing countries need talent and quality human capital to improve their citizens’ socio-economic conditions (Cooke, Reference Cooke and Tarique2021). Sadly, many are seeing a progressive erosion of their macro talent base (Lanvin & Monteiro, Reference Lanvin and Monteiro2019). Hence, these new findings call on leaders of developing countries to prioritise effective MTM investment to increase their macro talent base to achieve the SDG 3 on wellbeing and to accelerate convergence in wellbeing relative to their high performing peers. In particular, they should implement initiatives to attract and retain talent nationally through luring back skilled diaspora, and converting skilled minorities and migrants into national talents (Anlesinya, Reference Anlesinya2021; Khilji, Tarique, & Schuler, Reference Khilji, Tarique and Schuler2015) to increase their macro talent base.

Moreover, sustainable national funding sources should be identified and established to provide more talent development opportunities in the form of quality formal education, lifelong learning initiatives, establishment of more quality management institutions, capacity training and development interventions to cultivate macro talents and empower them in finding sustainable solutions to improve human lot and making citizens to live fulfilling lives. This is crucial as Khilji, Tarique, and Schuler (Reference Khilji, Tarique and Schuler2015) and Lanvin and Monteiro (Reference Lanvin and Monteiro2019) argued that programmes to grow macro talent base must pay attention to labour market, economic and educational systems.

Furthermore, developing countries should dedicate a specified proportion of revenues from the exploitation of their abundant natural capital to push MTM investment to develop more capable macro talents to properly manage natural capital to enhance wellbeing while mitigating impacts of resource curse on national wellbeing. Again, they should institute strong multi-layered accountability institutions/authorities to ensure efficient, transparent and effective utilisation of natural resource rent to improve macro talent base and national wellbeing. This is vital due to the threats of resource curse coupled with the fact that extraction of natural resources in most developing countries is often characterised by corruption and mismanagement, making it have less impact on the wellbeing of citizens than expected.